Research on Micro-video Multi-Label Classification Based on Deep Multimodal Association Learning
[Objective]The paper makes full use of the complementarity of modalities to enhance the correlation between modalities as well as between modalities and labels to achieve highly accurate classification effects.[Methods]We proposed a multi-label classification algorithm for micro-videos based on multimodal semantic enhancement and graph convolutional networks,utilizing multimodal information in micro-videos to support multi-label classification tasks.[Results]We verified the effectiveness of the proposed algorithm through a large number of experimental analyses,and the algorithm's classification accuracy reached 87.15％,which is 6.82％higher than the optimal benchmark algorithm.[Limitations]The process of modality fusion for information enhancement is hindered by the presence of redundant data,which in turn obscures the correlation between modalities.Furthermore,the domain of modality-based multi-label classification remains relatively unexplored with limited research available.[Conclusions]The algorithm effectively enhances the complementarity among modalities,strengthens the correlation between modalities and categories,and improves the accuracy of classification.
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